Given that the early data I’ve seen suggests that efficacy of 3 doses vs. omicron is similar to that of 2 doses vs. delta—probably a bit lower, but at least in the same universe—I’ve been using it largely as is, multiplying the final output by 2 to 3 based on what I’ve seen about the household transmission rate of Omicron relative to Delta. I know some other boosted people who have used it in a similar fashion. There’s so much uncertainty in the model assumptions that its best use in my view is to get very broad-strokes order-of-magnitude idea of the risk, which has been extremely useful for friends and relatives who have just wanted a baseline idea of whether the risk of getting COVID when participating in a particular activity is more like .01% or .1% or 1% or 10%. (Note: I doubt that said friends and relatives would have been able to use it in this way without my help, since it requires a little math and they’re not math types.) So I guess my main recommendations would be:
- don’t get rid of it even if you aren’t confident in the Omicron data—if you can produce results that are probably in the right order of magnitude, it’s still useful! If you aren’t up for a full Omicron overhaul, but you think there’s some back-of-the-envelope adjustment that could give results that are probably the correct order of magnitude, I think applying that—with suitable caveats about accuracy—would be preferable to taking the site down or leaving it as is.
- It’s easy to forget how many people are not math people whatsoever. Best practice in risk communication is often considered to be communicating numbers as percentages, as well as contextualized frequencies—not just ‘X-in-a-million’, but something like “X out of Y people (for context, Y is roughly the number of people living in Z)”—as there are a lot of people who don’t really understand percentages and need a little context to understand frequencies. In my ideal world the output would make the chance of getting COVID from this specific activity clear as a percentage and as a contextualized frequency, as well as the chance of getting COVID from this activity in a year under the assumption that you do this activity every N weeks, where N can be entered by the user.
Given that the early data I’ve seen suggests that efficacy of 3 doses vs. omicron is similar to that of 2 doses vs. delta—probably a bit lower, but at least in the same universe—I’ve been using it largely as is, multiplying the final output by 2 to 3 based on what I’ve seen about the household transmission rate of Omicron relative to Delta. I know some other boosted people who have used it in a similar fashion. There’s so much uncertainty in the model assumptions that its best use in my view is to get very broad-strokes order-of-magnitude idea of the risk, which has been extremely useful for friends and relatives who have just wanted a baseline idea of whether the risk of getting COVID when participating in a particular activity is more like .01% or .1% or 1% or 10%. (Note: I doubt that said friends and relatives would have been able to use it in this way without my help, since it requires a little math and they’re not math types.) So I guess my main recommendations would be:
- don’t get rid of it even if you aren’t confident in the Omicron data—if you can produce results that are probably in the right order of magnitude, it’s still useful! If you aren’t up for a full Omicron overhaul, but you think there’s some back-of-the-envelope adjustment that could give results that are probably the correct order of magnitude, I think applying that—with suitable caveats about accuracy—would be preferable to taking the site down or leaving it as is.
- It’s easy to forget how many people are not math people whatsoever. Best practice in risk communication is often considered to be communicating numbers as percentages, as well as contextualized frequencies—not just ‘X-in-a-million’, but something like “X out of Y people (for context, Y is roughly the number of people living in Z)”—as there are a lot of people who don’t really understand percentages and need a little context to understand frequencies. In my ideal world the output would make the chance of getting COVID from this specific activity clear as a percentage and as a contextualized frequency, as well as the chance of getting COVID from this activity in a year under the assumption that you do this activity every N weeks, where N can be entered by the user.